As user equipment is getting smarter, an identity authentication manner of the user equipment becomes increasingly diverse. Common identity authentication manners include password authentication, pattern authentication, peripheral authentication, and biometric feature-based identity authentication. Because the biometric feature-based identity authentication does not require memorization and features high portability, application of this type of authentication manner becomes increasingly common. Biometric feature-based identity authentication manners include fingerprint authentication, face authentication, voiceprint authentication, palmprint authentication, iris authentication, and the like.
Currently, in a biometric feature-based identity authentication method, user equipment first acquires current biometric feature data, then calculates a matching degree between the current biometric feature data and preconfigured biometric feature data, and finally determines whether the matching degree reaches a matching threshold, where if the matching degree reaches the matching threshold, it is determined that identity authentication succeeds; or if the matching degree does not reach the matching threshold, it is determined that identity authentication fails.
However, when it is determined whether the matching degree reaches the matching threshold, a surrounding environment of the user equipment imposes impact, for example, illumination intensity, noise intensity, and temperature in a current environment affect the current biometric feature data, and a location of the user equipment imposes impact, for example, a home, an office, or a restaurant in which the user equipment is located; consequently, the acquired current biometric feature data features relatively low accuracy, resulting in relatively low accuracy of identity authentication.